Published Jul 11, 2018

Dev Ops for Data Science

Kyle Polich delves into the synergy between DevOps and data science with experts Damien Brady, Paige Bailey, and Donovan Brown, highlighting the transformative role of Microsoft's DevOps tools in cloud integration, effective database management, and machine learning model deployment, emphasizing lifecycle management and workflow enhancement.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Kyle Polich

Dev Ops for Data Science

Topics covered

Popular Clips

Data Scientist's Deployment Choices

The Commoditization of AI

Microsoft's Unique Offering

Tool Trends

Creating Production-Like Environments

DevOps Insights

DevOps for Data Science

Listener Feedback Requested

Deploying Databases

Power of SSDT

Bridging Data Science and DevOps

DevOps Best Practices

DevOps for Databases

Database Rollbacks

Episode Highlights

Cloud and Tool Integration

Database Management

DevOps for Data Science

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ML Ops

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Dev Ops for Data Science

Topics covered

Popular Clips

Episode Highlights

Cloud and Tool IntegrationMicrosoft's DevOps tools stand out for their adaptability and open-source integration, fostering innovation across diverse platforms. Infrastructure as Code further enhances this by enabling efficient environment management, crucial for both large and small organizations.

Cloud and Tool Integration

Database ManagementDatabase deployment strategies are crucial for integrating databases into CI/CD pipelines, ensuring seamless updates and minimizing risks. Effective schema management tools like SSDT enhance developer capabilities and streamline database evolution.

Database Management

DevOps for Data Science

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